The performance of data-free compression can be improved by employing a generator for the reconstruction [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. [6]} proposes a framework for data-free knowledge distillation by exploiting generative adversarial networks, where the reconstructed training dataset derivated from the generator is expected to lead to maximum response on the discriminator of the pre-trained model. The KEGNET [2]} framework uses the generator and decoder networks to estimate the conditional distribution of the original training dataset for data-free knowledge distillation. In data-free knowledge distillation [3]}, an adversarial generator is used to produce and search for the reconstructed training dataset on which the student poorly match the teacher. In this paper, we improve on the work of [4]}, which proposes a knowledge matching generator to produce a reconstructed training dataset by exploiting classification boundary knowledge and distribution information from the pre-trained model.
